Artificial intelligence server

Abstract

An artificial intelligence server is disclosed. The artificial intelligence server includes an input unit to which input data is inputted, and a processor, when a first output value outputted by an artificial intelligence model with respect to first input data is correct and a second output value outputted by the artificial intelligence model with respect to second input data is incorrect, configured to use the first input data and the second input data to obtain a first domain causing an incorrect answer, and train the artificial intelligence model to be domain-adapted for the first domain.

What is claimed is: 
     
         1 . Artificial intelligence server comprising:
 an input interface to which input data is inputted; and   a processor, when a first output value outputted by an artificial intelligence model with respect to first input data is correct and a second output value outputted by the artificial intelligence model with respect to second input data is incorrect, configured to use the first input data and the second input data to obtain a first domain causing an incorrect answer, and train the artificial intelligence model to be domain-adapted for the first domain.   
     
     
         2 . The artificial intelligence server of  claim 1 , wherein when a third output value outputted by the trained artificial intelligence model with respect to third input data is correct and a fourth output value outputted by the trained artificial intelligence model with respect to fourth input data is incorrect, the processor obtains a second domain causing an incorrect answer using the third input data and the fourth input data; and re-trains the trained artificial intelligence model to be domain-adapted for the second domain,
 wherein the second domain is different from the first domain.   
     
     
         3 . The artificial intelligence server of  claim 2 , wherein when the artificial intelligence model outputs an output value using features corresponding to a plurality of domains, the processor obtains the first domain causing the most incorrect answer among the plurality of domains. 
     
     
         4 . The artificial intelligence server of  claim 3 , wherein when the artificial intelligence model outputs an output value using features corresponding to the plurality of domains, the processor obtains the first domain causing the most incorrect answer among the plurality of domains by using a distribution of the first input data and a distribution of the second input data for each of the plurality of domains. 
     
     
         5 . The artificial intelligence server of  claim 3 , wherein when the trained artificial intelligence model outputs an output value using features corresponding to the plurality of domains, the processor obtains the second domain causing the most incorrect answer among the plurality of domains. 
     
     
         6 . The artificial intelligence server of  claim 5 , wherein when the trained artificial intelligence model outputs an output value using features corresponding to the plurality of domains, the processor obtains the second domain causing the most incorrect answer among the plurality of domains by using a distribution of the first input data and a distribution of the second input data for each of the plurality of domains. 
     
     
         7 . The artificial intelligence server of  claim 2 , wherein the first domain comprises a  1 - 1  domain and a  1 - 2  domain,
 wherein the processor trains the artificial intelligence model to allow a feature extracted by the artificial intelligence model with respect to input data corresponding to the  1 - 1  domain and a feature extracted by the artificial intelligence model with respect to input data corresponding to the  1 - 2  domain to be mapped to the same area. 
 
     
     
         8 . The artificial intelligence server of  claim 7 , wherein the second domain comprises a  2 - 1  domain and a  2 - 2  domain,
 wherein the processor re-trains the trained artificial intelligence model to allow a feature extracted by the trained artificial intelligence model with respect to input data corresponding to the  2 - 1  domain and a feature extracted by the artificial intelligence model with respect to input data corresponding to the  2 - 2  domain to be mapped to the same area. 
 
     
     
         9 . The artificial intelligence server of  claim 7 , wherein the artificial intelligence model comprises:
 a feature extractor configured to extract the feature using input data;   a class classifier configured to classify classes using the extracted features; and   a domain classifier configured to classify domains using the extracted features.   
     
     
         10 . The artificial intelligence server of  claim 9 , wherein the processor trains the artificial intelligence model to allow the class classifier to classify the classes and prevent the domain classifier from classifying the  1 - 1  domain and the  1 - 2  domain. 
     
     
         11 . The artificial intelligence server of  claim 1 , wherein the processor obtains a second domain causing a second largest incorrect answer among a plurality of domains by using the first input data and the second input data, and re-trains the trained artificial intelligence model to be domain-adapted for the second domain. 
     
     
         12 . The artificial intelligence server of  claim 1 , wherein the processor selects an artificial intelligence model with the highest performance among a plurality of artificial intelligence models in which at least one of the number of domain adaptation, a target domain of domain adaptation, or the order of domain adaptation is different. 
     
     
         13 . The artificial intelligence server of  claim 12 , wherein the processor
 trains the artificial intelligence model to be domain-adapted for the first domain so as to generate a second artificial intelligence model,   trains the second artificial intelligence model to be domain-adapted for a second domain so as to generate a third artificial intelligence model, and   selects an artificial intelligence model with a higher performance among the second artificial intelligence model and the third artificial intelligence model.   
     
     
         14 . The artificial intelligence server of  claim 12 , wherein the processor
 trains the artificial intelligence model to be domain-adapted for the first domain so as to generate a second artificial intelligence model, and trains the second artificial intelligence model to be domain-adapted for the second domain so as to generate a third artificial intelligence model,   trains the artificial intelligence model to be domain-adapted for the second domain so as to generate a fourth artificial intelligence mode, and   selects an artificial intelligence model with higher performance among the third artificial intelligence model and the fourth artificial intelligence model.   
     
     
         15 . The artificial intelligence server of  claim 12 , wherein the processor
 trains the artificial intelligence model to be domain-adapted for the first domain so as to generate a second artificial intelligence model,   trains the second artificial intelligence model to be domain-adapted for the second domain so as to generate a third artificial intelligence mode, and   deletes the third artificial intelligence model from memory when a performance of the second artificial intelligence model among the second artificial intelligence model and the third artificial intelligence model is higher.   
     
     
         16 . The artificial intelligence server of  claim 12 , wherein the processor
 trains the artificial intelligence model to be domain-adapted for the first domain so as to generate a second artificial intelligence model,   trains the second artificial intelligence model to be domain-adapted for the second domain so as to generate a third artificial intelligence mode, and   does not additionally trains the third artificial intelligence model when a performance of the third artificial intelligence model is increased by less than a predetermined value compared to a performance of the second artificial intelligence model.   
     
     
         17 . The artificial intelligence server of  claim 12 , wherein the processor does not additionally train an artificial intelligence model that is not selected as an artificial intelligence model with the highest performance for more than a predetermined period among the plurality of artificial intelligence models. 
     
     
         18 . A domain adaptation method comprising:
 when a first output value outputted by an artificial intelligence model with respect to first input data is correct and a second output value outputted by the artificial intelligence model with respect to second input data is incorrect, using the first input data and the second input data to obtain a first domain causing an incorrect answer; and   training the artificial intelligence model to be domain-adapted for the first domain.   
     
     
         19 . The method of  claim 18 , further comprising:
 when a third output value outputted by the trained artificial intelligence model with respect to third input data is correct and a fourth output value outputted by the trained artificial intelligence model with respect to fourth input data is incorrect, obtaining a second domain causing an incorrect answer using the third input data and the fourth input data; and   re-training the trained artificial intelligence model to be domain-adapted for the second domain,   wherein the second domain is different from the first domain